{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第五周作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用Logistic回归技术实现糖尿病发病预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "问题描述\n",
    "一、 数据说明： Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集） 根据现有的医疗信息预测5年内皮马印第安人糖尿病发作的概率。\n",
    "数据链接：https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes\n",
    "p.s.: Kaggle也有一个Practice Fusion Diabetes Classification任务，可以试试:)\n",
    "https://www.kaggle.com/c/pf2012-diabetes \n",
    "\n",
    "解题提示\n",
    "1) 文件说明\n",
    "pima-indians-diabetes.csv：数据文件\n",
    "\n",
    "2) 字段说明\n",
    "数据集共9个字段:\n",
    "pregnants：怀孕次数\n",
    "Plasma_glucose_concentration：口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度\n",
    "blood_pressure：舒张压，单位:mm Hg\n",
    "Triceps_skin_fold_thickness：三头肌皮褶厚度，单位：mm\n",
    "serum_insulin：餐后血清胰岛素，单位:mm\n",
    "BMI：体重指数（体重（公斤）/ 身高（米）^2）\n",
    "Diabetes_pedigree_function：糖尿病家系作用\n",
    "Age：年龄\n",
    "Target：标签， 0表示不发病，1表示发病\n",
    "\n",
    "作业格式要求：\n",
    "juyter notebook格式，需包含：\n",
    "1 代码\n",
    "2 代码运行结果\n",
    "3 对代码与结果的说明与理解\n",
    "\n",
    "基础作业部分，可只提供代码运行结果（截图或log）和代码与结果，推荐markdown格式\n",
    "批改标准\n",
    "1. 对数据做数据探索分析（可参考0_EDA_ diabetes.ipynb，不计分）\n",
    "2. 适当的特征工程（可参考1_FE_ diabetes.ipynb，不计分）\n",
    "3. 采用5折交叉验证，分别用log似然损失和正确率，对Logistic回归模型的正则超参数调优。（各50分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 对数据做数据探索分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd  # 用于数据处理，CSV\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('pima-indians-diabetes.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看数据规模读取测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train： (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print('Train：', train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据基本信息\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集已知存在缺失值，某些列中存在的缺失值被标记为0.通过这些列中指标的定义和相应领域的常识可以证实上述观点，譬如体重指数和血压两列中的0作为指数数值来说是无意义的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果中我们可以看到很多列的最小值为0，而在一些特定列代表的变量中，0值并没有意义，这就表明该值无效或为缺失值。\n",
    "具体来说，下列变量的最小值为0时数据无意义：\n",
    "1. 血浆葡萄糖浓度 \n",
    "2. 舒张压 \n",
    "3. 肱三头肌皮褶厚度 \n",
    "4. 餐后血清胰岛素\n",
    "5. 体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第1、2、5列中0值较少；相比较而言，第3、4列中的0值多出数倍，接近总量的一半。 为了确保有足够的数据量来训练模型，针对不同的列需要有不同的缺失值判断策略"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看每个变量的分布  及其与标签之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# target\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')  # Diabetes糖尿病\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 怀孕次数pregnants\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#怀孕次数有超过17的？\n",
    "#但在疾病判断案例中，异常值可能就意味着得病，不能删除 \n",
    "ulimit=10\n",
    "#删除怀孕次数大于10的样本 \n",
    "train = train[train[\"pregnants\"]<ulimit]\n",
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77bce27e10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='pregnants', hue='Target', data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怀孕次数和是否得病从数据上看，有一定关系！！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 血浆葡萄糖浓度 Plasma_glucose_concentration\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "sns.distplot(train.Plasma_glucose_concentration, kde=False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77bccf41d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train, hue='Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAELCAYAAADDZxFQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFQBJREFUeJzt3Xu0ZnV93/H3BwYVUBkuIwu5OGNEDXElClPA0mUtpCmiAdIS5aIdCQltive0CiFrmbRdjSyNRtsGnIAyKopCSJgSiuII3qKDM0pAQMIEZBjK5WQpeGFFIHz7x94jx/F3Zh7mzHM557xfa511nv179nP2dz/PnPOZ32/v/dupKiRJ2tJO4y5AkjSZDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmhaNu4DZ2GeffWrp0qXjLkOS5pT169f/Q1Ut2dZ6czogli5dyrp168ZdhiTNKUnuHmQ9h5gkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVLT0AIiyUeSPJjk29Pa9kpybZI7+u979u1J8qEkG5LclOTQYdUlSRrMMHsQFwPHbtF2NrCmqg4G1vTLAK8CDu6/zgTOH2JdkqQBDO1K6qr6UpKlWzSfALyyf7wKuB54V9/+saoq4OtJFifZr6ruG1Z90lzxybUbB1rv1CMOGnIlWmhGfQxi32l/9O8H9u0f7w/cM229TX2bJGlMxnaQuu8t1FN9XZIzk6xLsm5qamoIlUmSYPQB8UCS/QD67w/27fcCB05b74C+7edU1cqqWl5Vy5cs2eZkhJKk7TTq2VxXAyuA9/Tfr5zW/qYklwJHAA97/EF6ajxWoR1taAGR5FN0B6T3SbIJeDddMHwmyRnA3cBr+9WvBo4DNgCPAKcPqy5J0mCGeRbTKTM8dUxj3QLOGlYtkqSnziupJUlNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqWnRuAuQNFqfXLtxm+ucesRBI6hEk84ehCSpyYCQJDUZEJKkJgNCktRkQEiSmsZyFlOStwO/DRRwM3A6sB9wKbA3sB54Q1U9Oo76pFEZ5IwiaVxG3oNIsj/wFmB5Vb0E2Bk4GTgP+EBVvQD4PnDGqGuTJD1pXENMi4BdkywCdgPuA44GLu+fXwWcOKbaJEmMISCq6l7gfcBGumB4mG5I6aGqerxfbROw/6hrkyQ9aRxDTHsCJwDLgOcCuwPHPoXXn5lkXZJ1U1NTQ6pSkjSOIaZfBe6qqqmqegy4AjgKWNwPOQEcANzbenFVrayq5VW1fMmSJaOpWJIWoHEExEbgyCS7JQlwDHArcB1wUr/OCuDKMdQmSeqN4xjEWrqD0d+kO8V1J2Al8C7gHUk20J3qetGoa5MkPWks10FU1buBd2/RfCdw+BjKkXY4r2/QfOCV1JKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmrYZEEn2HkUhkqTJMkgP4utJLktyXJIMvSJJ0kQYJCBeCKwE3gDckeR/JHnhcMuSJI3bNgOiOtdW1SnA7wArgBuSfDHJy4deoSRpLBZta4X+GMTr6XoQDwBvBlYDLwUuA5YNs0BJ0ngMMsT0NeDZwIlV9eqquqKqHq+qdcAF27PRJIuTXJ7kO0luS/LyJHsluTbJHf33PbfnZ0uSdoxBAuJFVfXfqmrTlk9U1Xnbud0PAtdU1YuBXwFuA84G1lTVwcCaflmSNCaDBMTnkizevJBkzySf3d4NJtkDeAVwEUBVPVpVDwEnAKv61VYBJ27vNiRJszdIQCzp/4ADUFXfB54zi20uA6aAjyb5VpILk+wO7FtV9/Xr3A/sO4ttSJJmaZCA+KckB21eSPI8oGaxzUXAocD5VfUy4MdsMZxUVTXTNpKcmWRdknVTU1OzKEOStDWDBMS5wFeSfDzJJ4AvAefMYpubgE1VtbZfvpwuMB5Ish9A//3B1ouramVVLa+q5UuWLJlFGZKkrRnkOohr6P6Afxq4FDisqrb7GERV3Q/ck+RFfdMxwK10p86u6NtWAFdu7zYkSbO3zesgek8Hvtevf0gSqupLs9jum4FLkjwNuBM4nS6sPpPkDOBu4LWz+PmSpFka5EK584DXAbcAT/TNRTfUtF2q6kZgeeOpY7b3Z0qj8sm1G8ddgjQSg/QgTqS7FuInwy5GkjQ5BjlIfSewy7ALkSRNlkF6EI8ANyZZA/y0F1FVbxlaVZKksRskIFb3X5KkBWSbAVFVq5LsChxUVbePoCZJ0gQY5Jajvw7cCFzTL780iT0KSZrnBjlI/YfA4cBD8NNTVJ8/xJokSRNgkIB4rKoe3qLtieaakqR5Y5CD1LckORXYOcnBwFuAvxluWZKkcRukB/Fm4JfoTnH9FPAD4G3DLEqSNH6DnMX0CN2MrucOvxxJ0qQYZC6m62jcm6Gqjh5KRZKkiTDIMYj/PO3xM4B/Bzw+nHIkSZNikCGm9Vs0fTXJDUOqR5I0IQYZYtpr2uJOwGHAHkOrSJI0EQYZYlpPdwwidENLdwFnDLMoSXPDIPfGOPWIg7a5jibTIENMy0ZRiCRpsgwyxPRvt/Z8VV2x48qRJE2KQYaYzgD+OfCFfvlf0V1JPUU39GRASNI8NEhA7AIcUlX3ASTZD7i4qk4famWSpLEaZKqNAzeHQ+8BwKNOkjTPDdKDWJPks3TzMAG8Dvj88EqSJE2CQc5ielOS3wBe0TetrKq/HG5ZkuaLQU6FBU+HnUSD9CAAvgn8sKo+n2S3JM+qqh8OszBJ0ngNcsvR3wEuBz7cN+0P/NUwi5Ikjd8gB6nPAo6iuw8EVXUH8JxhFiVJGr9BAuInVfXo5oUki2hM/y1Jml8GCYgvJvl9YNck/xq4DPg/wy1LkjRugwTE2XRXTd8M/AfgauAPhlmUJGn8tnoWU5KdgY9V1WnAn4+mJEnSJNhqD6Kq/gl4XpKnjageSdKEGOQ6iDvp7iK3Gvjx5saqev/QqpIkjd2MPYgkH+8fHg9c1a/7rGlfs5Jk5yTfSnJVv7wsydokG5J82l6LJI3X1noQhyV5LrAR+J9D2PZbgduAZ/fL5wEfqKpLk1xAN834+UPYriRpAFs7BnEBsAZ4IbBu2tf6/vt2S3IA8Grgwn45wNF0V2wDrAJOnM02JEmzM2NAVNWHquoXgY9W1fOnfS2rqufPcrt/CrwTeKJf3ht4qKoe75c30U3pIUkak21eB1FVv7sjN5jkNcCDVbV+O19/ZpJ1SdZNTU3tyNIkSdMMcqHcjnYUcHyS7wKX0g0tfRBY3E/jAXAAcG/rxVW1sqqWV9XyJUuWjKJeSVqQRh4QVXVOVR1QVUuBk4Ev9BfiXQec1K+2Arhy1LVJkp40jh7ETN4FvCPJBrpjEheNuR5JWtAGvWHQUFTV9cD1/eM7gcPHWY8k6UmT1IOQJE0QA0KS1GRASJKaDAhJUpMBIUlqGutZTOP0ybUbB1rv1CMOGnIlkjSZ7EFIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaFuyV1JJmNuhMA5rf7EFIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpNTbUg9p5eQfpY9CElSkz0ILQj2DqSnzh6EJKnJgJAkNRkQkqSmkQdEkgOTXJfk1iS3JHlr375XkmuT3NF/33PUtUmSnjSOHsTjwO9V1SHAkcBZSQ4BzgbWVNXBwJp+WZI0JiMPiKq6r6q+2T/+IXAbsD9wArCqX20VcOKoa5MkPWmsxyCSLAVeBqwF9q2q+/qn7gf2HVNZkiTGGBBJngn8BfC2qvrB9OeqqoCa4XVnJlmXZN3U1NQIKpWkhWksAZFkF7pwuKSqruibH0iyX//8fsCDrddW1cqqWl5Vy5csWTKagiVpARrHWUwBLgJuq6r3T3tqNbCif7wCuHLUtUmSnjSOqTaOAt4A3Jzkxr7t94H3AJ9JcgZwN/DaMdQmSeqNPCCq6itAZnj6mFHWIkmamVdSS5KaDAhJUpMBIUlqMiAkSU0GhCSpyTvKSZoIg9z179QjDhpBJdrMHoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU3eMEhz2iA3mdH8Mejn7Y2Fdgx7EJKkJgNCktRkQEiSmjwGIWlB2pHHr+brMQ97EJKkJnsQmlieoaTt5b+dHcMehCSpyYCQJDUZEJKkpok6BpHkWOCDwM7AhVX1njGXtCAMMl47X8/SkDSzielBJNkZ+N/Aq4BDgFOSHDLeqiRp4ZqkHsThwIaquhMgyaXACcCtY61qDhvHmRyePaKFaEfOETVJ801NTA8C2B+4Z9rypr5NkjQGk9SDGEiSM4Ez+8UfJbl9O3/UPsA/bGul07bzh4/QQPsxWyN4H0ayHyPgfkyWidqPWfwe/dx+zPJ38nmDrDRJAXEvcOC05QP6tp9RVSuBlbPdWJJ1VbV8tj9n3NyPyeJ+TBb3Y3YmaYjpG8DBSZYleRpwMrB6zDVJ0oI1MT2Iqno8yZuAz9Kd5vqRqrplzGVJ0oI1MQEBUFVXA1ePaHOzHqaaEO7HZHE/Jov7MQupqnFsV5I04SbpGIQkaYIsyIBIcmyS25NsSHL2uOsZVJIDk1yX5NYktyR5a9++V5Jrk9zRf99z3LVuS5Kdk3wryVX98rIka/vP5NP9iQoTLcniJJcn+U6S25K8fI5+Fm/v/z19O8mnkjxjLnweST6S5MEk357W1nz/0/lQvz83JTl0fJX/rBn24739v6ubkvxlksXTnjun34/bk/ybYda24AJijk/p8Tjwe1V1CHAkcFZf+9nAmqo6GFjTL0+6twK3TVs+D/hAVb0A+D5wxliqemo+CFxTVS8GfoVuf+bUZ5Fkf+AtwPKqegndCSInMzc+j4uBY7dom+n9fxVwcP91JnD+iGocxMX8/H5cC7ykqn4Z+DvgHID+9/1k4Jf61/xZ/zdtKBZcQDBtSo+qehTYPKXHxKuq+6rqm/3jH9L9Qdqfrv5V/WqrgBPHU+FgkhwAvBq4sF8OcDRweb/KXNiHPYBXABcBVNWjVfUQc+yz6C0Cdk2yCNgNuI858HlU1ZeA723RPNP7fwLwsep8HVicZL/RVLp1rf2oqs9V1eP94tfprguDbj8uraqfVNVdwAa6v2lDsRADYl5M6ZFkKfAyYC2wb1Xd1z91P7DvmMoa1J8C7wSe6Jf3Bh6a9gsxFz6TZcAU8NF+qOzCJLszxz6LqroXeB+wkS4YHgbWM/c+j81mev/n8u/9bwH/t3880v1YiAEx5yV5JvAXwNuq6gfTn6vutLSJPTUtyWuAB6tq/bhrmaVFwKHA+VX1MuDHbDGcNOmfBUA/Rn8CXeA9F9idnx/umJPmwvu/LUnOpRtavmQc21+IATHQlB6TKskudOFwSVVd0Tc/sLm73H9/cFz1DeAo4Pgk36Ub3juabix/cT/EAXPjM9kEbKqqtf3y5XSBMZc+C4BfBe6qqqmqegy4gu4zmmufx2Yzvf9z7vc+yRuB1wCn1ZPXI4x0PxZiQMzZKT36sfqLgNuq6v3TnloNrOgfrwCuHHVtg6qqc6rqgKpaSvfef6GqTgOuA07qV5vofQCoqvuBe5K8qG86hm5q+jnzWfQ2Akcm2a3/97V5P+bU5zHNTO//auDf92czHQk8PG0oauKku3naO4Hjq+qRaU+tBk5O8vQky+gOut8wtEKqasF9AcfRnRnw98C5467nKdT9L+i6zDcBN/Zfx9GN4a8B7gA+D+w17loH3J9XAlf1j5/f/0PfAFwGPH3c9Q1Q/0uBdf3n8VfAnnPxswD+CPgO8G3g48DT58LnAXyK7rjJY3Q9ujNmev+B0J29+PfAzXRnbY19H7ayHxvojjVs/j2/YNr65/b7cTvwqmHW5pXUkqSmhTjEJEkagAEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0JzXpKl06dKntZ+fZJZ3+g9yRuT/K/Z/hxprjEgpBGYNm3FvNqW5jcDQvPFoiSX9DfuuTzJbtOfTHJKkpv7m+KcN0D76Un+LskNdHMTzSjJxUkuSLKuf81r+vY3Jlmd5At0V/eS5L8k+UZ/I5g/6tt2T/LXSf62r+N1fft70t0c6qYk75u2rZOmbftH/fdXJvlyktV0U2WQ5PVJbkhyY5IPD/O+AZqf/J+G5osXAWdU1VeTfAT4T5ufSPJcuhvgHEZ385vPJTmRbiqJVvtauuknDqOb/vo64Fvb2P5Sunn5fwG4LskL+vZDgV+uqu8l+TW6uXMOp5v6YXWSVwBLgP9XVa/u690jyd7AbwAvrqqafkexrTiU7iYzdyX5ReB1wFFV9ViSPwNOAz42wM+RAANC88c9VfXV/vEn6O6Sttk/A66vqimAJJfQ3eynZmhni/ZPAy/cxvY/U1VPAHckuRN4cd9+bVVtvhnMr/Vfm8PmmXSB8WXgT/oezFVV9eV+mOgfgYvS3Zb1qgHegxuqu4kMdJPuHQZ8o5uDj12Z/JllNWEMCM0XW04qNupJxmba/o+ntQX446r68JYvTneP5OOA/55kTVX91ySH0/2hPwl4E93U6I/TDw0n2QmYfq/oLbe1qqrO2f5d0kLnMQjNFwcleXn/+FTgK9OeuwH4l0n26cfhTwG+uJX2tX373v39N35zgO3/ZpKdkvwC3UyotzfW+SzwW/0Nn0iyf5Ln9ENgj1TVJ4D3Aof26+xRVVcDb6e75zXAd+l6BgDHA7vMUM8a4KQkz+m3tVeS5w2wH9JP2YPQfHE7cFZ//OFWupvS/zp09/JOcjbdsYQAf11VVwJspf0Pga8BD9FNt7wtG+kC59nAf6yqf+yHdn6qqj7XHxv4Wv/cj4DXAy8A3pvkCbopn38XeBZwZZJn9LW9o/8xf963/y1wDT/ba5i+rVuT/AHdcZWd+p97FnD3APsiATjdtzRbSS6mO3Zw+bhrkXYkh5gkSU0OMUkDSncD+S2PR1xWVW8cQznS0DnEJElqcohJktRkQEiSmgwISVKTASFJajIgJElN/x/CYIXbe1Gy7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 血压 blood_pressure\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.blood_pressure, kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "血压为0？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'blood_pressure')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看blood_pressure 与标签之间的关系\n",
    "sns.violinplot(x='Target', y='blood_pressure', data=train, hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 三头肌皮褶厚度 Triceps_skin_fold_thickness\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Triceps_skin_fold_thickness, kde = False)\n",
    "plt.xlabel('Triceps_skin_fold_thickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there are some outliers in this feature. So let us remove them and then plot again.\n",
    "但在疾病判断案例中，异常值可能就意味着得病，不能删除\n",
    "\n",
    "ulimit = 80\n",
    "train = train[train['Triceps_skin_fold_thickness'] < ulimit]\n",
    "\n",
    "plt.scatter(range(train.shape[0]), train[\"Triceps_skin_fold_thickness\"].values,color='purple')\n",
    "plt.title(\"Distribution of Triceps_skin_fold_thickness\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Triceps_skin_fold_thickness', data=train, hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 餐后血清胰岛素 serum_insulin\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "sns.distplot(train.serum_insulin, kde=False)\n",
    "plt.xlabel('serum_insulin')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='serum_insulin', data=train, hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BMI\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "sns.distplot(train.BMI, kde=False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='BMI', data=train, hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BMI=0？ 为缺失值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b9831b00>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI','Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0, 1]].plot(kind='bar', stacked=True,figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从数据可以看出，BMI越高，患糖尿病的概率越大，也与尝试相符合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diabetes_pedigree_function 糖尿病家系作用\n",
    "fig=plt.figure()\n",
    "sns.distplot(train.Diabetes_pedigree_function, kde=False)\n",
    "plt.xlabel('Diabetes_pedigree_function')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b97c71d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Diabetes_pedigree_function','Target'])['Diabetes_pedigree_function'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄 Age\n",
    "fig=plt.figure()\n",
    "sns.distplot(train.Age, kde=False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b8e0cdd8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age','Target'])['Age'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b8df51d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lwF8m+eCs7Y8CfwDsA+4fQj5pIJx+0VGnqv4RWA1MzFr3fWA7cCu9q/pJY8mRuo46Sc4BVtC7eNLxszb9CfB3VfUfvQtYSuPHUtfR4sCcOvS+Rm59Vb07u7yr6jk860Vjzqs0SlJDnFOXpIZY6pLUEEtdkhpiqUtSQyx1SWqIpS5JDbHUJakhlrokNeR/AQkZ889psPc0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ofKHdS90x6HVca9dSOo/B5WIvbW1PS/LBdm33nUluTnJxW3ZWkn9vF0K7ZfbUfGmSGfQ63m1mcB33/wK+l+Qs4PeAdcAG4I0Mrks0e+2lvwcurqqzgKuBK8dRtLQYI/txcGlKbGFwYTYYnJq+hcH74pNV9XPggSS3tuVnAC8Cdg4uX8IJDC5BK000g17HrSQrGFyB89eTFIPgLh6/Bs6THgLcWVUvPUYlSkPh0I2OZxcD/1JVz6uqdVW1lsEvNT0E/H4bq18FnN/W3wvMJHlsKCfJC8dRuLQYBr2OZ1t48t77p4FfYXCN97uAf2Xw048PV9XPGHw4vDfJ14Dbgd88duVKS+PVK6UjSPKsqvpxu0Tul4CXteu+S1PHMXrpyG5qPxbzDOCvDHlNM/foJalzjtFLUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzv0fyw3wLeVWmWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature], kde=False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 适当的特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的工具包\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "train = pd.read_csv('pima-indians-diabetes.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "粗看数据集没有缺失值但该数据已知存在缺失值，某些列中存在的缺失值被标记为0.通过这些列中指标的定义和相应领域的常识可以证实上述观点，譬如体重指数和血压两列中的0作为指数数值来说是无意义的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果中我们可以看到很多列的最小值为0，而在一些特定列代表的变量中，0值并没有意义，这就表明该值无效或为缺失值。 具体来说，下列变量的最小值为0时数据无意义：\n",
    "\n",
    "  1. 血浆葡萄糖浓度\n",
    "  2. 舒张压\n",
    "  3. 肱三头肌皮褶厚度\n",
    "  4. 餐后血清胰岛素\n",
    "  5. 体重指数\n",
    "  \n",
    "     在Pandas的DataFrame中，通过replace()函数可以很方便的将我们感兴趣的数据子集的值标记为NaN。\n",
    "     标记完缺失值之后，可以利用ishull()函数将数据集中所有的NaN值标记为True，然后就可以得到每一列中缺失值的数量类。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0,np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对缺失值较多的特征，新增一个特征，表示这个特征是否缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>Triceps_skin_fold_thickness_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Triceps_skin_fold_thickness  Triceps_skin_fold_thickness_Missing\n",
       "0                         35.0                                    0\n",
       "1                         29.0                                    0\n",
       "2                          NaN                                    1\n",
       "3                         23.0                                    0\n",
       "4                         35.0                                    0\n",
       "5                          NaN                                    1\n",
       "6                         32.0                                    0\n",
       "7                          NaN                                    1\n",
       "8                         45.0                                    0\n",
       "9                          NaN                                    1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Triceps_skin_fold_thickness缺失值较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "train['Triceps_skin_fold_thickness_Missing'] = train['Triceps_skin_fold_thickness'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b7123908>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(x='Triceps_skin_fold_thickness_Missing', hue='Target', data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>serum_insulin_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>543.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   serum_insulin  serum_insulin_Missing\n",
       "0            NaN                      1\n",
       "1            NaN                      1\n",
       "2            NaN                      1\n",
       "3           94.0                      0\n",
       "4          168.0                      0\n",
       "5            NaN                      1\n",
       "6           88.0                      0\n",
       "7            NaN                      1\n",
       "8          543.0                      0\n",
       "9            NaN                      1"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# serum_insulin缺失值较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "train['serum_insulin_Missing'] = train['serum_insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['serum_insulin','serum_insulin_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b70eaeb8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='serum_insulin_Missing', hue='Target', data = train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从数据上观察，特征是否缺失好像和目标没什么关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为特征是否缺失好像和目标没什么关系，感觉特征缺失是随机的，所以删除新增的特征，老老实实用中值填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['serum_insulin_Missing','Triceps_skin_fold_thickness_Missing'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用中值填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medians = train.median()\n",
    "\n",
    "# fillna()会将DataFrame中nan数据的数据填充为想要的数据，并返回填充后的结果。如果希望在原DataFrame中修改，则把inplace设置为True。\n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())  # isnull().sum()就更加直观了，它直接告诉了我们每列缺失值的数量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "# 分离特征和目标\n",
    "y_train = train['Target']\n",
    "X_train = train.drop(['Target'], axis=1)\n",
    "\n",
    "# 用于保存特征工程之后的结果\n",
    "feat_names = X_train.columns\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征处理结果存为文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 存为csv格式\n",
    "X_train = pd.DataFrame(columns=feat_names, data = X_train)\n",
    "\n",
    "train = pd.concat([X_train, y_train], axis=1)\n",
    "\n",
    "train.to_csv('FE_pima-indians-diabetes_lpw.csv',index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 采用5折交叉验证，分别用log似然损失和正确率，对Logistic回归模型的正则超参数调优。（各50分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1)采用log似然损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log损失评估的交叉验证后的最佳得分： 0.4760264118497699\n",
      "log损失评估的最佳超参数为： {'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的工具包\n",
    "# Logistic Regression + GridSearchCV 超参数调整 \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 读入数据\n",
    "train = pd.read_csv('FE_pima-indians-diabetes_lpw.csv')\n",
    "\n",
    "# 分离特征和目标\n",
    "y_train = train['Target']\n",
    "X_train = train.drop(['Target'], axis=1)\n",
    "\n",
    "# 保存特征名字以备后用\n",
    "feat_names = X_train.columns\n",
    "# 1.设置参数搜索范围\n",
    "penaltys = ['l1', 'l2']\n",
    "Cs = [0.001,0.01,0.1,1,10,100,1000]\n",
    "\n",
    "# 2.生成学习器实例\n",
    "lr_penalty = LogisticRegression(solver='liblinear')\n",
    "\n",
    "# 3.生成GridSearchCV的实例\n",
    "tuned_parameters = dict(penalty=penaltys,C=Cs)\n",
    "grid_log = GridSearchCV(lr_penalty, tuned_parameters, cv=5, scoring='neg_log_loss', n_jobs=4)  # 5折交叉验证，log似然损失\n",
    "# 4.调用GridSearchCV的fit方法\n",
    "grid_log.fit(X_train, y_train)\n",
    "# 打印训练结果\n",
    "print('log损失评估的交叉验证后的最佳得分：', -grid_log.best_score_)\n",
    "print('log损失评估的最佳超参数为：', grid_log.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "采用log似然损失为模型评价指标时：最佳超参数为C=1，采用L1正则。此时的负log损失最小，为0.476"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （2）采用正确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确率评估的交叉验证后的最佳得分： 0.7747395833333334\n",
      "正确率的最佳超参数为： {'C': 0.1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression + GridSearchCV 超参数调整 \n",
    "\n",
    "# 1.设置参数搜索范围\n",
    "penaltys = ['l1', 'l2']\n",
    "Cs = [0.001,0.01,0.1,1,10,100,1000]\n",
    "\n",
    "# 2.生成学习器实例\n",
    "lr_penalty = LogisticRegression(solver='liblinear')\n",
    "\n",
    "# 3.生成GridSearchCV的实例\n",
    "tuned_parameters = dict(penalty=penaltys,C=Cs)\n",
    "grid_accuracy = GridSearchCV(lr_penalty, tuned_parameters, cv=5, scoring='accuracy', n_jobs=4)  #5折交叉验证，正确率评价指标\n",
    "# 4.调用GridSearchCV的fit方法\n",
    "grid_accuracy.fit(X_train,y_train)\n",
    "# 打印训练结果\n",
    "print('正确率评估的交叉验证后的最佳得分：', grid_accuracy.best_score_)\n",
    "print('正确率的最佳超参数为：', grid_accuracy.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "采用正确率为模型评价指标时：最佳超参数为C=0.1，采用L2正则。此时正确率最高为0.77474"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不足之处\n",
    "在采用Logistic RegressionCV进行超参数调优的时候，对fit后如何获取评价值和最优超参数不太清楚，感觉还不如用Logistic+GridSearchCV方便，直接调用GridSearchCV.best_score_/.best_params_就可以了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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